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Prediction of academic achievement for college computer science majors in the Republic of China

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dc.contributor.advisor Niess, Margaret L.
dc.creator Fan, Tai-Sheng
dc.date.accessioned 2012-10-16T17:52:34Z
dc.date.available 2012-10-16T17:52:34Z
dc.date.copyright 1996-04-05
dc.date.issued 1996-04-05
dc.identifier.uri http://hdl.handle.net/1957/34439
dc.description Graduation date: 1996 en_US
dc.description.abstract The purpose of this research was to determine whether student academic achievement in college computer science programs in the Republic of China (ROC) could be predicted by factors reported to be effective in US studies. The relationship between these factors and course performance in computer science programs was examined. Gender differences were also interrogated. Sophomore, junior, and senior students enrolled in five universities offering computer science programs in the ROC constituted the population. A researcher-designed questionnaire was used to collect background information. Validity and reliability issues were addressed by the conduct of validity assessment, questionnaire pilot testing, and interviews with selected pilot test subjects. Scores from the College Entrance Examination (CEE) and college computer science courses were accessed through university registrar's offices. A total of 940 questionnaires were collected, representing more than 81% of the population. From data analysis, the predictive powers of CEE test scores in relation to subsequent college performance appeared to be limited. The CEE math component was negatively correlated to performance in college computer science programs. The positive relation of math ability to academic achievement in complete computer science programs was confirmed. High school overall achievement as well as math course averages were identified as effective performance predictors for college computer science programs. Prior computer experience showed no conclusive relationship to subsequent performance in college computer science courses. The close relationship between performance in beginning computer science courses and performance in complete computer science programs was validated. Significant linear prediction models with limited predictive powers (R2 ranged from 0.19 to 0.30) were generated for overall performance, but not for introductory computer science course performance. Model predictive powers were significantly improved (R2 range from 0.59 to 0.63) when performance in introductory computer science courses was included in the models. Significant gender differences were not found for CEE performance, prior computer experience, and prediction models. However, female subjects outperformed male counterparts in course performance at both the high school and college levels. en_US
dc.language.iso en_US en_US
dc.subject.lcsh College students -- China en_US
dc.subject.lcsh Science students -- China en_US
dc.subject.lcsh Prediction of scholastic success -- China en_US
dc.title Prediction of academic achievement for college computer science majors in the Republic of China en_US
dc.type Thesis/Dissertation en_US
dc.degree.name Doctor of Philosophy (Ph. D.) in Science Education en_US
dc.degree.level Doctoral en_US
dc.degree.discipline Science en_US
dc.degree.grantor Oregon State University en_US
dc.description.digitization File scanned at 300 ppi (Monochrome) using ScandAll PRO 1.8.1 on a Fi-6670 in PDF format. CVista PdfCompressor 4.0 was used for pdf compression and textual OCR. en_US
dc.description.peerreview no en_us


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